We introduce a finite-element-model-updating-based open-source framework to identify mechanical parameters of heterogeneous hyperelastic materials from in silico generated full-field data which can be downloaded here https://github.com/aflahelouneg/inverse_identification_soft_tissue. The numerical process consists in simulating an extensometer performing in vivo uniaxial tensile experiment on a soft tissue. The reaction forces and displacement fields are respectively captured by force sensor and Digital Image Correlation techniques. By means of a forward nonlinear FEM model and an inverse solver, the model parameters are estimated through a constrained optimization function with no quadratic penalty term. As a case study, our Finite Element Model Updating (FEMU) tool has been applied on a model composed of a keloid scar surrounded by healthy skin. The results show that at least 4 parameters can be accurately identified from an uniaxial test only. The originality of this work lies in two major elements. Firstly, we develop a low-cost technique able to characterize the mechanical properties of heterogeneous nonlinear hyperelastic materials. Secondly, we explore the model accuracy via a detailed study of the interplay between discretization error and the error due to measurement uncertainty. Next steps consist in identifying the real parameters and so finding the matching preferential directions of keloid scars growth.

我们引入了一个基于有限元模型更新的开源框架，用于从计算机生成的全场数据中识别异质超弹性材料的机械参数，该数据可以在此处下载 https://github.com/aflahelouneg/inverse_identification_soft_tissue。数值过程包括模拟在体内执行的引伸计软组织单轴拉伸实验。反作用力和位移场分别由力传感器和数字图像相关技术捕获。通过前向非线性有限元模型和逆求解器，通过没有二次惩罚项的约束优化函数来估计模型参数。作为案例研究，我们的有限元模型更新 (FEMU) 工具已应用于由健康皮肤包围的瘢痕疙瘩疤痕组成的模型。结果表明，仅通过单轴试验就可以准确识别至少 4 个参数。这部作品的独创性在于两大要素。首先，我们开发了一种能够表征异质非线性超弹性材料力学性能的低成本技术。第二，我们通过详细研究离散化误差和测量不确定性引起的误差之间的相互作用来探索模型的准确性。接下来的步骤包括识别真实参数，从而找到瘢痕疙瘩生长的匹配优先方向。